Success Story: Pipe flow measurements, analysis and analytics

NCC presenting the success story

Croatian Competence Centre (HR HPC CC) provides end users from scientific and higher education communities, various industries and public administrations, access to innovative solutions adapted to the level of maturity of national and European High-Performance Computing (HPC) infrastructure. HR HPC CC helps strengthen existing and develop new national competencies for High-Performance Computing (HPC), High-Performance Data Analytics (HPDA) and the area of Artificial Intelligence (AI).

Industrial Organisations Involved:

Adria-electronic d.o.o. is a company founded in 1974 that specializes in intelligent electrical systems, automation and control. The company’s main products are intelligent sensing systems for hotel rooms, building automatization systems and electrical energy sensing equipment and software.

Technical/scientific Challenge:

SME partner required help with ultrasonic pipe measurements, general sensing and automation. Therefore, a series of experimental test measurements had to be conducted, obtained experimental data had to be analysed, and subsequently used to improve current systems that integrate different sensing technologies.


NCC Croatia along with the Department of Fluid Mechanics and Computational Engineering at the Faculty of Engineering, University of Rijeka, assisted with big data analytics of flow meter oscilloscope measurements. Members of the Faculty conducted experimental measurements in order to obtain large datasets which were subsequently used to statistically analyse an SME-defined system. Due to the complexity of the datasets, computational requirements were significant hence the use of extensive computational resources was required.


  • Insight/knowledge of the system behaviour
  • Gathered information and conclusions will facilitate development of future products and solutions


  • Keywords: Data Analytics, Big Data, Measurements, Sensors, Automation
  • Industry sector: Mechanical engineering
  • Technology: Big Data


Lado Kranjčević

This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 951732. The JU receives support from the European Union’s Horizon 2020 research and innovation program and Germany, Bulgaria, Austria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, the United Kingdom, France, the Netherlands, Belgium, Luxembourg, Slovakia, Norway, Switzerland, Turkey, Republic of North Macedonia, Iceland, Montenegro